Bagging vs Boosting

Question: Explain bagging and boosting. Key points: Bagging (Bootstrap Aggregating) trains multiple models independently and in parallel on bootstrapped samples of the data, then averages/votes their predictions to reduce variance (e.g. Random Forest). Boosting trains models sequentially, with each new model focused on correcting the errors of the ensemble so far, reducing bias (e.g. AdaBoost, Gradient Boosting/XGBoost). Bagging is generally more robust to overfitting; boosting often achieves higher accuracy but needs careful regularization.